Fall detection for elderly-people monitoring using learned features and recurrent neural networks

AbstractElderly care is becoming a relevant issue with the increase of population ageing. Fall injuries, with their impact on social and healthcare cost, represent one of the biggest concerns over the years. Researchers are focusing their attention on several fall-detection algorithms. In this paper, we present a deep-learning solution for automatic fall detection from RGB videos. The proposed approach achieved a mean recall of 0.916, prompting the possibility of translating this approach in the actual monitoring practice. Moreover to enable the scientific community making research on the topic the dataset used for our experiments will be released. This could enhance elderly people safety and quality of life, attenuating risks during elderly activities of daily living with reduced healthcare costs as a final result

Benchmarking of Dual-Step Neural Networks for Detection of Dangerous Weapons on Edge Devices

Nowadays, criminal activities involving hand-held weapons are widespread throughout the world and pose a significant problem for the community. The development of Video Surveillance Systems (VSV) and Artificial Intelligence (AI) approaches have made it possible to implement automatic systems for detecting dangerous weapons even in crowded environments. However, the detection of hand-held weapons - usually very small in size with respect to the Field of View (FoV) of the camera - is still an open challenge. The use of complex hardware systems and deep learning (DL) architectures have mitigated this problem and achieved excellent results, but involve high costs and high performance that hinder the deployment of such systems. In this contest, we present a comprehensive performance comparison in terms of inference time and detection accuracy of two low-cost edge devices: Google Coral Dev board and NVIDIA Jetson Nano. We deployed and run on both boards a dual-step DL framework for hand-held weapons detection exploiting half-precision floating-point (FP16) quantization on Jetson Nano and 8-bit signed integer (INT8) quantization on Coral Dev. Our results show that both in terms of PASCAL VOC mean Average Precision (mAP) and Frames per Second (FPS), the framework running on Jetson Nano (mAP = 99.6, FPS = 4.5, 2.5, 1.7, 1.4 from 1 to 4 people in the camera FoV, respectively) slightly outperform the Coral's one (mAP = 98.8 and FPS = 2.9. 1.5, 1.1, 0.9 from 1 to 4 people in the camera FoV, respectively). The Coral Dev obtained the highest inference speed (FPS = 36.5) overcoming the Jetson Nano (FPS=23.8) only when running the dual-step framework with no people in the camera FoV. In conclusion, the benchmark on the two edge devices points out that both allow to run the framework with satisfactory results, pushing towards the diffusion of such on-the-edge systems in a real-world scenario

Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation

: Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting preterm-birth complications. With such a view, in this work we propose a deep-learning framework for preterm infants' pose estimation from depth videos acquired in the actual clinical practice. The pipeline consists of two consecutive convolutional neural networks (CNNs). The first CNN (inherited from our previous work) acts to roughly predict joints and joint-connections position, while the second CNN (Asy-regression CNN) refines such predictions to trace the limb pose. Asy-regression relies on asymmetric convolutions to temporally optimize both the training and predictions phase. Compared to its counterpart without asymmetric convolutions, Asy-regression experiences a reduction in training and prediction time of 66% , while keeping the root mean square error, computed against manual pose annotation, merely unchanged. Research mostly works to develop highly accurate models, few efforts have been invested to make the training and deployment of such models time-effective. With a view to make these monitoring technologies sustainable, here we focused on the second aspect and addressed the problem of designing a framework as trade-off between reliability and efficiency

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

Background and objectives: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patient management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. Methods: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. Results: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. Discussion and conclusions: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria

TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images

Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainable deep-learning algorithm for accurate limb-pose estimation from depth images. The algorithm consists of a convolutional neural network (TwinEDA) relying on architectural blocks that require limited computation while ensuring high performance in prediction. To ascertain its low computational costs and assess its application in on-the-edge computing, TwinEDA was additionally deployed on a cost-effective single-board computer. The network was validated on a dataset of 27,000 depth video frames collected during the actual clinical practice from 27 preterm infants. When compared to the main state-of-the-art competitor, TwinEDA is twice as fast to predict a single depth frame and four times as light in terms of memory, while performing similarly in terms of Dice similarity coefficient (0.88). This result suggests that the pursuit of efficiency does not imply the detriment of performance. This work is among the first to propose an automatic and sustainable limb-position estimation approach for preterm infants. This represents a significant step towards the development of broadly accessible clinical monitoring applications